Stemming versus Light Stemming for measuring the simitilarity between Arabic Words with Latent Semantic Analysis model

Representation of semantic information contained in the words is needed for any Arabic Text Mining applications. More precisely, the purpose is to better take into account the semantic dependencies between words expressed by the co-occurrence frequencies of these words. There have been many proposals to compute similarities between words based on their distributions in contexts. In this paper, we compare and contrast the effect of two preprocessing techniques applied to Arabic corpus: Stemming, and Light Stemming techniques for measuring the semantic between Arabic words with the well known abstractive model -Latent Semantic Analysis (LSA)- with a wide variety of distance functions and similarity measures, such as the Euclidean Distance, Cosine Similarity, Jaccard Coefficient, and the Pearson Correlation Coefficient. The obtained results show that, on the one hand, the variety of the corpus produces more accurate results; on the other hand, the Light Stemming outperformed the Stemming approach because Stemming affects the words meanings.